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mlflow-docker-compose

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MLflow deployment with 1 command

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Download Latest Version (.zip)

- Dockerfile
- requirements.txt
.env
docker-compose.yml
LICENSE
README.md
renovate.json
wait-for-it.sh

# Installation Guide

1. Get the code

git clone https://github.com/sachua/mlflow-docker-compose

Downloads the entire project code from GitHub to your computer.

cd mlflow-docker-compose

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install mlflow boto3

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. Docker

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Docker Desktop Needed to build and run containers. Install it and keep it running in the background.

git clone https://github.com/sachua/mlflow-docker-compose.git

Downloads the entire project code from GitHub to your computer.

docker compose up -d --build

Builds and starts all defined containers (server, database, etc.) at once, in the background.

✅ Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.

Pulled directly from this repo's README.

4. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install mlflow boto3

Installs the package published on PyPI directly — no need to clone the source.

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# MLflow On-Premise Deployment using Docker Compose
Easily deploy an MLflow tracking server with 1 command.

MinIO S3 is used as the artifact store and MySQL server is used as the backend store.

## How to run

1. Clone (download) this repository

```bash
    git clone https://github.com/sachua/mlflow-docker-compose.git
    ```

2. `cd` into the `mlflow-docker-compose` directory

3. Build and run the containers with `docker-compose`

```bash
    docker compose up -d --build
    ```

4. Access MLflow UI with http://localhost:5000

5. Access MinIO UI with http://localhost:9000

## Containerization

The MLflow tracking server is composed of 3 docker containers:

* MLflow server
* MinIO object storage server
* MySQL database server

## Example

1. Install [conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html)

2. Install MLflow with extra dependencies, including scikit-learn

```bash
    pip install mlflow boto3
    ```

3. Set environmental variables

```bash
    export MLFLOW_TRACKING_URI=http://localhost:5000
    export MLFLOW_S3_ENDPOINT_URL=http://localhost:9000
    ```
4. Set MinIO credentials

```bash
    cat <<EOF > ~/.aws/credentials
    [default]
    aws_access_key_id=minio
    aws_secret_access_key=minio123
    EOF
    ```

5. Train a sample MLflow model

```bash
    mlflow run https://github.com/sachua/mlflow-example.git -P alpha=0.42
    ```

6. Serve the model (replace ${MODEL_ID} with your model's ID)
    ```bash
    export MODEL_ID=0ced24069348417fbbcb2cd41a7d2f07 # Replace this with your model's ID
    mlflow models serve -m runs:/${MODEL_ID}/model -p 1234 --env-manager conda
    ```

7. You can check the input with this command
    ```bash
    curl -X POST -H "Content-Type:application/json" --data '{"dataframe_split":{"columns":["fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density", "pH", "sulphates", "alcohol"],"data":[[6.2, 0.66, 0.48, 1.2, 0.029, 29, 75, 0.98, 3.33, 0.39, 12.8]]}}' http://127.0.0.1:1234/invocations
    ```